Social Media Sponsorship: Metrics for Finding the Right Content Creator-Sponsor Matches

Abstract

Social media video sponsorship, in which a sponsor forms a partnership with a content creator and sponsors video content, has become increasingly popular. Using rich data on sponsored and non-sponsored videos on Facebook, we introduce two metrics, Content Similarity and Audience Closeness, to help sponsors find effective creator-sponsor matches. Content Similarity is a content-based metric that measures video topic similarity between creators and sponsors. Audience Closeness is a network-based metric that measures how close creators and sponsors are in the network and captures the similarity in their audiences. We find that both metrics significantly increase video viewership. We additionally examine how congruency metrics can be used in combination with other sponsorship strategies that affect key message structure factors. In particular, we examine how the message source credibility (i.e., credibility of a content creator), message novelty (i.e., formation of new partnership), and the consumer’s beliefs about the source’s intention to persuade (i.e., sponsor’s presence in the content) affect the associative link process and moderate the effects of metrics on the performance of sponsored videos. Overall, the two novel metrics offer valuable insights for finding effective creator-sponsor matches.

Keywords: Social Media Video Sponsorship; Content Creator; Sponsor; Sponsorship Relevance; Natural Language Processing; Latent Dirichlet Allocation; Machine Learning; Matrix Completion

JEL Classification: M31; M37